Leveraging RRT^*: Probabilistically Interpreted Mechanisms Enhanced With P-HOPE and FLEX-OPT for Complex Path Planning by Yujie Miao, Haiyang Liu, Ziqiang Zhang, Yanju Liang

“…The experimental results suggest that the proposed RRT<inline-formula> <tex-math notation="LaTeX">$^{\ast }$ </tex-math></inline-formula>-PRIME algorithm can reduce the initial solution search time by 76.32%, reduce the number of search nodes by about 80.67%, and improve the search path quality compared with the RRT<inline-formula> <tex-math notation="LaTeX">$^{\ast }$ </tex-math></inline-formula> algorithm. …”
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Enhancing Guided Wave Tomography for Improved DefectDetection in Critical Structures by Igor Sakhabiev, Maolin Li, Younho Cho, Young H Kim

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OPTIMIZING THE PLACEMENT OF A BATCH OF WORK-PIECES AT A MULTI-POSITION ROTARY TABLE OF TRANSFER MACHINE by N. N. Guschinsky, B. M. Rozin

“…The experiments revealed that the PSO algorithm on average is more effective for the solution of the problem compared to methods of random search and LP-search.…”
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Methodology for Studying Neural Network Descriptors in Solving the Problem of Finding Anatomical Layers in Computed Tomography Images of the Lungs by A. A. Kosareva

“…The paper proposes a methodology for comparison of neural network descriptors and selection of an optimal neural network method for searching for similar anatomical regions. Neural network approaches are compared with traditional methods and a hybrid search algorithm based on the joint use of traditional and neural network methods is proposed. …”
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An Improved Symbiosis Particle Swarm Optimization for Solving Economic Load Dispatch Problem by Jianxia Zhang, Jianxin Zhang, Feng Zhang, Minglu Chi, Linbin Wan

“…From the simulation results, it can be seen clearly that the costs of electric-power generation gained were the lowest compared with the results of particle swarm optimization algorithm, improved chaos particle swarm optimization algorithm, and symbiotic organisms search algorithm, well demonstrating the effectiveness of the improved symbiosis particle swarm optimization algorithm in solving the economic load dispatch problem.…”
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Maximum power point tracking of photovoltaic module based on Particle Swarm Optimization enhanced with Quasi-Newton method. by Montaser Abdelsattar, Hamdi Ali Mohamed, Mohamed A Ismeil, Ahmed A Zaki Diab

“…MPPT proposal algorithm results are compared to the results of the hybrid PSO-P&O algorithm at different operating conditions. …”
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A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection by Zheng Huang, Chengling Jiang, Chao Shen, Bin Liu, Tao Huang, Minghui Zhang

“…Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. …”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…To address this problem, the velocity update equation of the original PSO algorithm is modified by incorporating a dynamic linear decreasing inertia weight, which improves the PSO algorithm’s convergence behaviour and aids both local and global search. …”
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Geological disaster risk assessment based on remote sensing, GIS, and a multi-strategy improved GWO-optimized Elman neural network by Kuayue Chen, Fuhong Bai, Ting Wang, Yingmei Su, Baoyun Wang

“…In the model design, strategies including chaotic reverse learning, nonlinear convergence control, Levy flight perturbation, and self-history best are introduced to enhance the global search capability and convergence stability of the algorithm. …”
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The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture. by Xiying Wang

“…This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. …”
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A Multi-Objective PSO-GWO Approach for Smart Grid Reconfiguration with Renewable Energy and Electric Vehicles by Tung Linh Nguyen, Quynh Anh Nguyen

“…While the Particle Swarm Optimization algorithm is renowned for its rapid convergence and effective exploitation of solution spaces, its capacity to thoroughly explore complex search domains remains limited, particularly in multifaceted optimization challenges. …”
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Improving the Incident Management Process Based on a Use Case Approach by A. A. Mikryukov, A. V. Kuular

“…A distinctive feature of the developed algorithm is the application of a use case recognition algorithm and the search for similar images contained in the base of use cases using the nearest neighbor method.…”
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Adaptive lift chiller units fault diagnosis model based on machine learning. by Yang Guo, Zengrui Tian, Hong Wang, Mengyao Chen, Pan Chu, Yingjie Sheng

“…HINGO enhances the uniformity of the initial population distribution by means of refraction opposition-based learning strategy in initialization, and improves the local and global search ability of the algorithm by means of sine and cosine strategy, Lévy flight and nonlinear decreasing factor in the search stage. …”
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Optimization method for data processing in digital twin-enabled computing power network for industrial production by WU Shuang, XIE Renchao, TANG Qinqin, XIE Gaochang, HUANG Tao

“…Simulation results show that, compared with benchmark algorithms, the proposed algorithm provides better model placement decisions and demonstrates superior global optimum search capabilities.…”
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A Differential Evolution Based MPPT Method for Photovoltaic Modules under Partial Shading Conditions by Kok Soon Tey, Saad Mekhilef, Hong-Tzer Yang, Ming-Kai Chuang

“…Therefore, this paper proposes a differential evolution (DE) based optimization algorithm to provide the globalized search space to track the GMPP. …”
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Robot path planning based on improved ant colony optimization by XUE Xiang, ZHU Qixin, ZHU Yonghong

“…Simulation results show that compared with the traditional algorithm, the improved algorithm reduces the convergence time by 3%, the shortest path length by 12%, and the number of convergence iterations by 76%. …”
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Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing by Yun Li, Lingxia Liao, Shanlin Sun, Zhicheng Tan, Xing Yao

“…Compared with the genetic algorithm and search space size methods, the structural sequence search proposed by pilot design algorithm for co-sparse channel estimation based on compressed sensing is used to design the pilot to complete the channel estimation. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

“…Multi-strategy fusion enables the sea-horse optimizer (SHO) to perform dynamic exploitation and exploration in the early stage of iteration, expand the search scope initially, and narrow the search scope in the middle and later stages of the algorithm, so as to avoid the algorithm falling into the local optimal and increase the possibility of the algorithm jumping out of the local optimal, and avoid the blind search caused by elite influence. …”
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Results of specialized outpatient follow-up of patients with embolic stroke of undetermined source by P. I. Merkureva, J. D. Bogatenkova, I. A. Voznyuk, V. A. Sorokoumov, E. A. Popov

“…Patients with ESUS are an extremely difficult category of patients for diagnostic search, for whom the actual task is to develop an examination algorithm for the earliest possible detection of the cause of stroke and to determine the optimal methods of secondary prevention.…”
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The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task by Jingye He, Junpeng Shao, Guitao Sun

“…DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. …”
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